This proposal brings together a team of applied mathematicians and experimental physicists, engineers, and biologists, with expertise in biogels, mucus physics, microbiology and bacterial motility, and gastroenterology to tackle an important problem in physiology and pathology: how the gastric mucus layer is maintained and how it responds to infecting bacteria and to changes in topology and size in gastric organoids (GOs). Cells in the stomach epithelium secrete the mucin that forms a mucus layer to protect the epithelium from the harsh environment of the stomach lumen, which is acidic and contains digestive enzymes such as pepsin. Epithelial cells also secrete acid, neutralizing bicarbonate, and pepsinogen, the inactive precursor to pepsin. These secretions form a complex coupled system since the rheology of mucin depends on pH and ionic strength, acid can be bound by negatively charged mucin, ions and mucin electrostatically interact, pepsinogen activation is pH dependent, and pepsin catalyzes mucin degradation. Goal #1 of this proposal is to understand how this coupled system maintains homeostasis. Goal #2 is to understand infection by Helicobacter pylori, which must swim across the mucus layer to colonize the epithelium. It locally modifies the gel rheology as it swims by secreting neutralizing ammonia. Goal #3 is to understand whether gastric organoids (GOs), spherical 3D cultures of a monolayer of differentiated epithelial cells, can accurately model gastric mucus layer physiology and pathology. The approach is to A: Build a mathematical model that fully couples mucin, ion, and enzyme transport and interactions. Validate it through in vitro experiments on acid transport through mucin. B: Investigate mechanisms of mucus layer homeostasis and acid transport using the mathematical model, flat 2D layers of cultured epithelium, and physical models of mucus, by exploring volumetric, spatial, and temporal variations of secretion rates. C: Mathematically model interaction of swimming H. pylori with mucus and experimentally image and track single bacteria together with local ion concentrations and micro-rheology. Model and experimentally observe collective effects of infection by dense populations of bacteria. D: Model and experimentally test how variations in size and spatial localization of secretion affect mucus layer formation in GOs to learn how and when they may be used as accurate models of physiology/pathology.